A Hybrid Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Unit Architecture for Protein Secondary Structure Prediction

Abstract

Protein secondary structure prediction plays a pivotal role in deciphering protein function and structure, with implications for drug discovery, functional annotation, and molecular biology research. Deep learning techniques, particularly recurrent neural networks (RNNs), have shown promise in capturing sequential dependencies and contextual information from protein sequences. In this study, we propose a novel approach for protein secondary structure prediction by integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) architectures. Leveraging the complementary strengths of BiLSTM and BiGRU networks, our integrated model aims to enhance prediction accuracy and robustness by effectively capturing both short and long-range dependencies within protein sequences. We evaluate the performance of the proposed model on benchmark datasets and compare it with state-of-the-art methods in the field. The model is trained on CB6133 filtered dataset and tested on CB513, CASP13 and CASP14 dataset. Our results demonstrate 87.92% and 78.6% Q3 and Q8 accuracy on CB513 dataset.

Keywords

• Protein secondary structure prediction
• bidirectional LSTM
• bidirectional GRU